- Training a model to classify images between different classes .
- This single package lets us harness the power of the state of the art models without any hassle of coding them ourselves.
- Just 3 lines of code and we're done.
pip install imageGenie
https://colab.research.google.com/drive/1DGgrENv-XTVeRz7PsOm0tpofJFZWn6PU?usp=sharing
- Fully Automated Mode
- Folder Structure
main folder
from imageGenie.classify import Classifier # import the Classifier Class
cl = Classifier("/root", "/models") # arg1 -> base directory containing train & test ; arg2 -> saving directory
cl.run() # this trains the model by automatically finding out number of classes, types of images and optimum training epochs.
- Controlled mode (Work in progress)
- Handle all image formats
- Parse the specifications provided by the uer from a config file. That may include the priority of speed, accuracy, emphasis on False Positives or negatives, time available to experiment and train.
- Include all other model architectures like EfficientNet, MobileNet, Inception, VGG.
- Algorithm to figure out what architecture and hyper-params would be the best (in the fully automated mode) as per hardware.
- Save all other artefacts like pipeline, metrics, plots, etc
- Allow user to construct a model by themselves
- Allow to either have a proper folder structure or a json with labels.